May 29, 2029AIExplore

How to Use Cohere for Command text generation

Learn Cohere command text generation with step by step workflows, realistic examples, and verified plan notes.

Cohere works well for command text generation when you run it like production work: locked brief, SOURCE facts, then review before publish. Cohere provides enterprise AI models and the North platform for secure deployment, including Command, Embed, Rerank, and Transcribe with VPC and on-premises options. Confirm live pricing on cohere.com/pricing. Start at /explore/cohere.

This guide focuses on command text generation in detail. Related Cohere articles: /blog/how-to-use-cohere-for-embed-and-rag-pipelines, /blog/how-to-use-cohere-for-rerank-relevance-tuning, /blog/how-to-use-cohere-for-transcribe-workflows.

When this workflow is the right job

Use command text generation when the deliverable is specifically this Cohere job. Switch to embed and rag pipelines when that workflow already owns the asset.

Step by step workflow

1. Brief Command text generation

Write what must stay true for command text generation in Cohere before settings or spend.

Brief: Command text generation
Keep: verified SOURCE facts only
Avoid: invented pricing or features
Success: one reviewable output

2. Open Cohere for Command text generation

Use the Cohere surface that owns command text generation. Do not mix a neighboring workflow in the same pass.

Surface: Command text generation
Start: pilot with one representative input
Plans: cohere.com/pricing

3. Pilot Command text generation

Run a single command text generation pilot. Score clarity, grounding, and whether the output is reviewable.

Pilot: Command text generation
[ ] SOURCE facts match
[ ] Output reviewable
[ ] Settings logged

4. Refine Command text generation

Change one command text generation dimension only. Save a template from the best run.

Refine: Command text generation
Change: one control only
Keep: SOURCE and success criteria

Practical command text generation examples

Summarize endpoint

Scenario:
An engineer prototypes Command text generation via Cohere for "Summarize endpoint".

Objective:
Ship a reviewable API/prompt result for Summarize endpoint with spend controls.

Inputs:
- Prompt and schema for Summarize endpoint
- Model/endpoint choice
- Token/spend limits
- Safety filters

Workflow:
Create key → Configure Command text generation → Pilot Summarize endpoint → Log usage → Add retries/filters → Productionize

Requirements:
- Stay within verified Cohere capabilities; do not invent features.
- Confirm live plan notes on cohere.com/pricing before promising volume.
- Change one variable between iterations.
- Human-review before external publish, send, billing, or clinical/legal use.

Expected output:
A working Command text generation prototype for Summarize endpoint with sample output and monitoring notes.

Router model pick

Scenario:
An engineer prototypes Command text generation via Cohere for "Router model pick".

Objective:
Ship a reviewable API/prompt result for Router model pick with spend controls.

Inputs:
- Prompt and schema for Router model pick
- Model/endpoint choice
- Token/spend limits
- Safety filters

Workflow:
Create key → Configure Command text generation → Pilot Router model pick → Log usage → Add retries/filters → Productionize

Requirements:
- Stay within verified Cohere capabilities; do not invent features.
- Confirm live plan notes on cohere.com/pricing before promising volume.
- Change one variable between iterations.
- Human-review before external publish, send, billing, or clinical/legal use.

Expected output:
A working Command text generation prototype for Router model pick with sample output and monitoring notes.

Stream tokens

Scenario:
An engineer prototypes Command text generation via Cohere for "Stream tokens".

Objective:
Ship a reviewable API/prompt result for Stream tokens with spend controls.

Inputs:
- Prompt and schema for Stream tokens
- Model/endpoint choice
- Token/spend limits
- Safety filters

Workflow:
Create key → Configure Command text generation → Pilot Stream tokens → Log usage → Add retries/filters → Productionize

Requirements:
- Stay within verified Cohere capabilities; do not invent features.
- Confirm live plan notes on cohere.com/pricing before promising volume.
- Change one variable between iterations.
- Human-review before external publish, send, billing, or clinical/legal use.

Expected output:
A working Command text generation prototype for Stream tokens with sample output and monitoring notes.

Spend alert

Scenario:
An engineer prototypes Command text generation via Cohere for "Spend alert".

Objective:
Ship a reviewable API/prompt result for Spend alert with spend controls.

Inputs:
- Prompt and schema for Spend alert
- Model/endpoint choice
- Token/spend limits
- Safety filters

Workflow:
Create key → Configure Command text generation → Pilot Spend alert → Log usage → Add retries/filters → Productionize

Requirements:
- Stay within verified Cohere capabilities; do not invent features.
- Confirm live plan notes on cohere.com/pricing before promising volume.
- Change one variable between iterations.
- Human-review before external publish, send, billing, or clinical/legal use.

Expected output:
A working Command text generation prototype for Spend alert with sample output and monitoring notes.

Cache safe replies

Scenario:
An engineer prototypes Command text generation via Cohere for "Cache safe replies".

Objective:
Ship a reviewable API/prompt result for Cache safe replies with spend controls.

Inputs:
- Prompt and schema for Cache safe replies
- Model/endpoint choice
- Token/spend limits
- Safety filters

Workflow:
Create key → Configure Command text generation → Pilot Cache safe replies → Log usage → Add retries/filters → Productionize

Requirements:
- Stay within verified Cohere capabilities; do not invent features.
- Confirm live plan notes on cohere.com/pricing before promising volume.
- Change one variable between iterations.
- Human-review before external publish, send, billing, or clinical/legal use.

Expected output:
A working Command text generation prototype for Cache safe replies with sample output and monitoring notes.

Safety filter

Scenario:
An engineer prototypes Command text generation via Cohere for "Safety filter".

Objective:
Ship a reviewable API/prompt result for Safety filter with spend controls.

Inputs:
- Prompt and schema for Safety filter
- Model/endpoint choice
- Token/spend limits
- Safety filters

Workflow:
Create key → Configure Command text generation → Pilot Safety filter → Log usage → Add retries/filters → Productionize

Requirements:
- Stay within verified Cohere capabilities; do not invent features.
- Confirm live plan notes on cohere.com/pricing before promising volume.
- Change one variable between iterations.
- Human-review before external publish, send, billing, or clinical/legal use.

Expected output:
A working Command text generation prototype for Safety filter with sample output and monitoring notes.

Bilingual draft

Scenario:
An engineer prototypes Command text generation via Cohere for "Bilingual draft".

Objective:
Ship a reviewable API/prompt result for Bilingual draft with spend controls.

Inputs:
- Prompt and schema for Bilingual draft
- Model/endpoint choice
- Token/spend limits
- Safety filters

Workflow:
Create key → Configure Command text generation → Pilot Bilingual draft → Log usage → Add retries/filters → Productionize

Requirements:
- Stay within verified Cohere capabilities; do not invent features.
- Confirm live plan notes on cohere.com/pricing before promising volume.
- Change one variable between iterations.
- Human-review before external publish, send, billing, or clinical/legal use.

Expected output:
A working Command text generation prototype for Bilingual draft with sample output and monitoring notes.

System prompt lock

Scenario:
An engineer prototypes Command text generation via Cohere for "System prompt lock".

Objective:
Ship a reviewable API/prompt result for System prompt lock with spend controls.

Inputs:
- Prompt and schema for System prompt lock
- Model/endpoint choice
- Token/spend limits
- Safety filters

Workflow:
Create key → Configure Command text generation → Pilot System prompt lock → Log usage → Add retries/filters → Productionize

Requirements:
- Stay within verified Cohere capabilities; do not invent features.
- Confirm live plan notes on cohere.com/pricing before promising volume.
- Change one variable between iterations.
- Human-review before external publish, send, billing, or clinical/legal use.

Expected output:
A working Command text generation prototype for System prompt lock with sample output and monitoring notes.

Temp 0.2

Scenario:
An engineer prototypes Command text generation via Cohere for "Temp 0.2".

Objective:
Ship a reviewable API/prompt result for Temp 0.2 with spend controls.

Inputs:
- Prompt and schema for Temp 0.2
- Model/endpoint choice
- Token/spend limits
- Safety filters

Workflow:
Create key → Configure Command text generation → Pilot Temp 0.2 → Log usage → Add retries/filters → Productionize

Requirements:
- Stay within verified Cohere capabilities; do not invent features.
- Confirm live plan notes on cohere.com/pricing before promising volume.
- Change one variable between iterations.
- Human-review before external publish, send, billing, or clinical/legal use.

Expected output:
A working Command text generation prototype for Temp 0.2 with sample output and monitoring notes.

Max tokens note

Scenario:
An engineer prototypes Command text generation via Cohere for "Max tokens note".

Objective:
Ship a reviewable API/prompt result for Max tokens note with spend controls.

Inputs:
- Prompt and schema for Max tokens note
- Model/endpoint choice
- Token/spend limits
- Safety filters

Workflow:
Create key → Configure Command text generation → Pilot Max tokens note → Log usage → Add retries/filters → Productionize

Requirements:
- Stay within verified Cohere capabilities; do not invent features.
- Confirm live plan notes on cohere.com/pricing before promising volume.
- Change one variable between iterations.
- Human-review before external publish, send, billing, or clinical/legal use.

Expected output:
A working Command text generation prototype for Max tokens note with sample output and monitoring notes.

Retry backoff

Scenario:
An engineer prototypes Command text generation via Cohere for "Retry backoff".

Objective:
Ship a reviewable API/prompt result for Retry backoff with spend controls.

Inputs:
- Prompt and schema for Retry backoff
- Model/endpoint choice
- Token/spend limits
- Safety filters

Workflow:
Create key → Configure Command text generation → Pilot Retry backoff → Log usage → Add retries/filters → Productionize

Requirements:
- Stay within verified Cohere capabilities; do not invent features.
- Confirm live plan notes on cohere.com/pricing before promising volume.
- Change one variable between iterations.
- Human-review before external publish, send, billing, or clinical/legal use.

Expected output:
A working Command text generation prototype for Retry backoff with sample output and monitoring notes.

Eval set

Scenario:
An engineer prototypes Command text generation via Cohere for "Eval set".

Objective:
Ship a reviewable API/prompt result for Eval set with spend controls.

Inputs:
- Prompt and schema for Eval set
- Model/endpoint choice
- Token/spend limits
- Safety filters

Workflow:
Create key → Configure Command text generation → Pilot Eval set → Log usage → Add retries/filters → Productionize

Requirements:
- Stay within verified Cohere capabilities; do not invent features.
- Confirm live plan notes on cohere.com/pricing before promising volume.
- Change one variable between iterations.
- Human-review before external publish, send, billing, or clinical/legal use.

Expected output:
A working Command text generation prototype for Eval set with sample output and monitoring notes.

PII scrub

Scenario:
An engineer prototypes Command text generation via Cohere for "PII scrub".

Objective:
Ship a reviewable API/prompt result for PII scrub with spend controls.

Inputs:
- Prompt and schema for PII scrub
- Model/endpoint choice
- Token/spend limits
- Safety filters

Workflow:
Create key → Configure Command text generation → Pilot PII scrub → Log usage → Add retries/filters → Productionize

Requirements:
- Stay within verified Cohere capabilities; do not invent features.
- Confirm live plan notes on cohere.com/pricing before promising volume.
- Change one variable between iterations.
- Human-review before external publish, send, billing, or clinical/legal use.

Expected output:
A working Command text generation prototype for PII scrub with sample output and monitoring notes.

Latency budget

Scenario:
An engineer prototypes Command text generation via Cohere for "Latency budget".

Objective:
Ship a reviewable API/prompt result for Latency budget with spend controls.

Inputs:
- Prompt and schema for Latency budget
- Model/endpoint choice
- Token/spend limits
- Safety filters

Workflow:
Create key → Configure Command text generation → Pilot Latency budget → Log usage → Add retries/filters → Productionize

Requirements:
- Stay within verified Cohere capabilities; do not invent features.
- Confirm live plan notes on cohere.com/pricing before promising volume.
- Change one variable between iterations.
- Human-review before external publish, send, billing, or clinical/legal use.

Expected output:
A working Command text generation prototype for Latency budget with sample output and monitoring notes.

Single-tenant note

Scenario:
An engineer prototypes Command text generation via Cohere for "Single-tenant note".

Objective:
Ship a reviewable API/prompt result for Single-tenant note with spend controls.

Inputs:
- Prompt and schema for Single-tenant note
- Model/endpoint choice
- Token/spend limits
- Safety filters

Workflow:
Create key → Configure Command text generation → Pilot Single-tenant note → Log usage → Add retries/filters → Productionize

Requirements:
- Stay within verified Cohere capabilities; do not invent features.
- Confirm live plan notes on cohere.com/pricing before promising volume.
- Change one variable between iterations.
- Human-review before external publish, send, billing, or clinical/legal use.

Expected output:
A working Command text generation prototype for Single-tenant note with sample output and monitoring notes.

Key rotate

Scenario:
An engineer prototypes Command text generation via Cohere for "Key rotate".

Objective:
Ship a reviewable API/prompt result for Key rotate with spend controls.

Inputs:
- Prompt and schema for Key rotate
- Model/endpoint choice
- Token/spend limits
- Safety filters

Workflow:
Create key → Configure Command text generation → Pilot Key rotate → Log usage → Add retries/filters → Productionize

Requirements:
- Stay within verified Cohere capabilities; do not invent features.
- Confirm live plan notes on cohere.com/pricing before promising volume.
- Change one variable between iterations.
- Human-review before external publish, send, billing, or clinical/legal use.

Expected output:
A working Command text generation prototype for Key rotate with sample output and monitoring notes.

Log redaction

Scenario:
An engineer prototypes Command text generation via Cohere for "Log redaction".

Objective:
Ship a reviewable API/prompt result for Log redaction with spend controls.

Inputs:
- Prompt and schema for Log redaction
- Model/endpoint choice
- Token/spend limits
- Safety filters

Workflow:
Create key → Configure Command text generation → Pilot Log redaction → Log usage → Add retries/filters → Productionize

Requirements:
- Stay within verified Cohere capabilities; do not invent features.
- Confirm live plan notes on cohere.com/pricing before promising volume.
- Change one variable between iterations.
- Human-review before external publish, send, billing, or clinical/legal use.

Expected output:
A working Command text generation prototype for Log redaction with sample output and monitoring notes.

Fallback model

Scenario:
An engineer prototypes Command text generation via Cohere for "Fallback model".

Objective:
Ship a reviewable API/prompt result for Fallback model with spend controls.

Inputs:
- Prompt and schema for Fallback model
- Model/endpoint choice
- Token/spend limits
- Safety filters

Workflow:
Create key → Configure Command text generation → Pilot Fallback model → Log usage → Add retries/filters → Productionize

Requirements:
- Stay within verified Cohere capabilities; do not invent features.
- Confirm live plan notes on cohere.com/pricing before promising volume.
- Change one variable between iterations.
- Human-review before external publish, send, billing, or clinical/legal use.

Expected output:
A working Command text generation prototype for Fallback model with sample output and monitoring notes.

Pilot prompt

Scenario:
An engineer prototypes Command text generation via Cohere for "Pilot prompt".

Objective:
Ship a reviewable API/prompt result for Pilot prompt with spend controls.

Inputs:
- Prompt and schema for Pilot prompt
- Model/endpoint choice
- Token/spend limits
- Safety filters

Workflow:
Create key → Configure Command text generation → Pilot Pilot prompt → Log usage → Add retries/filters → Productionize

Requirements:
- Stay within verified Cohere capabilities; do not invent features.
- Confirm live plan notes on cohere.com/pricing before promising volume.
- Change one variable between iterations.
- Human-review before external publish, send, billing, or clinical/legal use.

Expected output:
A working Command text generation prototype for Pilot prompt with sample output and monitoring notes.

Chat completion

Scenario:
An engineer prototypes Command text generation via Cohere for "Chat completion".

Objective:
Ship a reviewable API/prompt result for Chat completion with spend controls.

Inputs:
- Prompt and schema for Chat completion
- Model/endpoint choice
- Token/spend limits
- Safety filters

Workflow:
Create key → Configure Command text generation → Pilot Chat completion → Log usage → Add retries/filters → Productionize

Requirements:
- Stay within verified Cohere capabilities; do not invent features.
- Confirm live plan notes on cohere.com/pricing before promising volume.
- Change one variable between iterations.
- Human-review before external publish, send, billing, or clinical/legal use.

Expected output:
A working Command text generation prototype for Chat completion with sample output and monitoring notes.

JSON schema out

Scenario:
An engineer prototypes Command text generation via Cohere for "JSON schema out".

Objective:
Ship a reviewable API/prompt result for JSON schema out with spend controls.

Inputs:
- Prompt and schema for JSON schema out
- Model/endpoint choice
- Token/spend limits
- Safety filters

Workflow:
Create key → Configure Command text generation → Pilot JSON schema out → Log usage → Add retries/filters → Productionize

Requirements:
- Stay within verified Cohere capabilities; do not invent features.
- Confirm live plan notes on cohere.com/pricing before promising volume.
- Change one variable between iterations.
- Human-review before external publish, send, billing, or clinical/legal use.

Expected output:
A working Command text generation prototype for JSON schema out with sample output and monitoring notes.

How to improve command text generation

Cut noise from command text generation by removing extra adjectives while preserving SOURCE facts in Cohere.

Raise quality by insisting on a single success check before debating style.

Make review easier by labeling fields that must never change.

Speed iteration by cloning the last good run and altering only one control.

Stabilize outputs by pinning settings after the pilot is approved.

Reduce rework by rejecting drafts that invent claims.

Improve handoffs by recording which control produced the best result.

Harden the workflow by testing an incomplete input before trusting defaults.

Prompting and usage guidance

Name the command text generation job, audience, and success check before opening Cohere.

Paste only verified facts under SOURCE so Cohere cannot invent details.

Specify the deliverable shape up front.

Call out fixed details versus flexible style choices.

Ask Cohere to flag unsupported claims before you accept the draft.

Limitations to respect

Check Cohere plan gates for command text generation on cohere.com/pricing before you promise timelines.

Keep drafts unpublished until a human confirms SOURCE facts.

Cohere can be wrong. Treat command text generation as provisional until review.

If documentation is silent on a claim, leave it out rather than guessing.

Practical tips for this workflow

Pilot once before batching command text generation in Cohere.

Keep a reusable template with variables for command text generation.

Separate creative instructions from SOURCE facts.

Log settings from the best run.

Common mistakes

Skipping the pilot run before scaling volume
Inventing pricing, quotas, or features not on official pages
Mixing unrelated workflows in one session
Publishing without a human review gate

Treat command text generation in Cohere as a production workflow: brief, pilot, refine, then ship with review. Related reading: /blog/how-to-use-cohere-for-embed-and-rag-pipelines, /blog/how-to-use-cohere-for-rerank-relevance-tuning, /blog/how-to-use-cohere-for-transcribe-workflows.